Cited by 407Battery Modeling and ManagementAdvancements in Battery MaterialsGaussian Processes and Bayesian Inference

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Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

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Richardson, Robert R., Birkl, Christoph R., Osborne, Michael A., Howey, David A.·2019·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2018.2794997

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Abstract

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2%-3% root-mean-squared error (RMSE).

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Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Untitled work
(2012) · Cited by 9,062
Functional Data Analysis
Ramsay, J. O. (2005) · Wiley eBooks · doi:10.1002/0471667196.ess3138 · Cited by 2,908
Degradation diagnostics for lithium ion cells
Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.011 · Cited by 1,945 · cited 3×
“Li-ion cells undergo three primary modes of degradation: loss of lithium inventory, loss of active positive electrode material, and loss of active negative electrode material [17].”
BackgroundMethodSelf-citation
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.032 · Cited by 1,927
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
Main aging mechanisms in Li ion batteries
Broussely, M., Biensan, P., Bonhomme, F., Blanchard, P., Herreyre, S., Nechev, K., & Staniewicz, R. J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.03.172 · Cited by 1,162
Understanding the Degradation Mechanisms of LiNi 0.5 Co 0.2 Mn 0.3 O 2 Cathode Material in Lithium Ion Batteries
Jung, S., Gwon, H., Hong, J., Park, K., Seo, D., Kim, H., Hyun, J., Yang, W.-Y., & Kang, K. (2013) · Advanced Energy Materials · doi:10.1002/aenm.201300787 · Cited by 1,143
Synthesize battery degradation modes via a diagnostic and prognostic model
Dubarry, M., Truchot, C., & Liaw, B. Y. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.07.016 · Cited by 1,065

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Research works printed but not linked: 3

grewal, 2011, Kalman Filtering
bole, 2014, Randomized battery usage data set, NASA Ames Prognostics Data Repository
moura, 0, PDE estimation techniques for advanced battery management systems, Proc Amer Control Conf, 566

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Authors: Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A.
Affiliations: University of Oxford (GB)
ORCID: Richardson, Robert R.
Osborne, Michael A.
Howey, David A.
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-01
Volume: 15
Issue: 1
Pages: 127-138
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2018.2794997
OpenAlex: W2772728162
Cited by: 407
References: 45
Citations by year: 2026: 46 · 2025: 59 · 2024: 58 · 2023: 62 · 2022: 70 · 2021: 61 · 2020: 29 · 2019: 18
FWCI: 14.88
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Engineering and Physical Sciences Research Council (EP/K002252/1)
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords: lithium-ion batteries, battery capacity estimation, Gaussian process regression, galvanostatic operation, battery diagnostics

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Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

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